1. Field of the Invention:
This invention relates generally to a method and system for automated processing of medical images using feature-extraction techniques, and more particularly, to an automated method and system for the detection and classification of abnormal regions in digital medical images.
2. Discussion of the Background:
Detection and classification of abnormal lesions and distortions in radiographs, such as masses and parenchymal distortions in breast radiographs, so called mammograms, are among the most important and difficult tasks performed by radiologists.
Breast cancer is the most common malignancy occurring in women and its incidence is rising. Breast cancer will occur in approximately one out of every ten women sometime during their lifetime. At present, mammography is the most effective method for the early detection of breast cancer. Studies indicate that 26% of nonpalpable cancers present mammographically as a mass while 18% present both with a mass and microcalcifications. Thus, many breast cancers are detected and referred for surgical biopsy on the basis of a radiographically detected mass lesion.
Visual characteristics currently used by radiologists to distinguish between malignant and benign lesions include analysis of the contour of the mass, the degree of associated parenchymal retraction and distortion, and the density of the mass. Although general rules for the differentiation between benign and malignant breast lesions exist, considerable error in the classification of lesions occurs with the current methods of radiographic characterization. In fact, on average, only 10-20% of masses referred for surgical breast biopsy are actually malignant.
Surgical biopsy is an invasive technique that is an expensive and traumatic experience for the patient and leaves physical scars that may hinder later diagnoses (to the extent of requiring repeat biopsies for a radiographic tumor-simulating scar). In addition, the miss rate for the radiographic detection of malignant lesions ranges from 12 to 30 percent. A computer scheme capable of detecting and analyzing the characteristics of benign and malignant lesions and parenchymal distortions, in an objective manner, should aid radiologists by reducing the numbers of false-negative and false-positive diagnoses of malignancies, thereby decreasing patient morbidity as well as the number of surgical biopsies performed and their associated complications.
Long term studies of patients have shown that prognosis of breast cancer depends on the size of the tumor at the onset of treatment. Various studies have indicated that regular mammographic screening can reduce the mortality from breast cancer in women. The American Cancer Society has strongly recommended the use of mammography for the early detection of breast cancer. Their present recommendations include obtaining a baseline mammogram on all asymptomatic women over the age of 35 followed by biannular examinations between the ages of 40 and 49, with annular examinations after the age of 50. Thus, mammography may become one of the largest volume X-ray procedures routinely interpreted by radiologists. It is apparent that the efficiency and effectiveness of screening procedures could be increased substantially by use of a computer system that successfully aids the radiologist in detecting lesions and making diagnostic decisions.
Several investigators have attempted to analyze mammographic abnormalities with computers (See Winsberg et al, Radiology 89: 211-215, 1967; Ackerman et al., Cancer 30: 1025-1035, 1932; Ackerman et al., Cancer 31: 342-352, 1973; Fox et al., Proc. IEEE, 5th International Conference on Pattern Recognition: 624-631, 1980; and Magnin et al., Optical Engineers, 25: 780-784, 1986). Of those attempted, feature-extraction techniques were used without utilizing the bilateral symmetry information of the left and right mammograms. In addition, the spatial frequency characteristics of the spiculations of suspected lesions were not considered. Basically, the known earlier studies failed to achieve an accuracy acceptable for clinical practice. Chan. et al, Proc. SPIE 767: 367-370, 1987 have reported that successful attempts have been made in the detection of microcalcifications in digital mammograms (but not for lesions and parenchymal distortions).